NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
### **NNI [v0.5.1](https://github.com/Microsoft/nni/releases) has been released!**
### **NNI [v0.5.2](https://github.com/Microsoft/nni/releases) has been released!**
To improve user experience and reduce user effort, we design an annotation grammar. Using NNI annotation, users can adapt their code to NNI just by adding some standalone annotating strings, which does not affect the execution of the original code.
To improve user experience and reduce user effort, we design an annotation grammar. Using NNI annotation, users can adapt their code to NNI just by adding some standalone annotating strings, which does not affect the execution of the original code.
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@@ -32,7 +31,30 @@ In NNI, there are mainly four types of annotation:
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@@ -32,7 +31,30 @@ In NNI, there are mainly four types of annotation:
-**sampling_algo**: Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an `nni.` identification and a search space type specified in [SearchSpaceSpec](SearchSpaceSpec.md) such as `choice` or `uniform`.
-**sampling_algo**: Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an `nni.` identification and a search space type specified in [SearchSpaceSpec](SearchSpaceSpec.md) such as `choice` or `uniform`.
-**name**: The name of the variable that the selected value will be assigned to. Note that this argument should be the same as the left value of the following assignment statement.
-**name**: The name of the variable that the selected value will be assigned to. Note that this argument should be the same as the left value of the following assignment statement.
An example here is:
There are 10 types to express your search space as follows:
Which means the variable value is a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.
-**\*functions**: Several functions that are waiting to be selected from. Note that it should be a complete function call with arguments. Such as `max_pool(hidden_layer, pool_size)`.
- **functions**: Several functions that are waiting to be selected from. Note that it should be a complete function call with arguments. Such as `max_pool(hidden_layer, pool_size)`.
- **name**: The name of the function that will be replaced in the following assignment statement.
- **name**: The name of the function that will be replaced in the following assignment statement.
@@ -47,17 +47,17 @@ All types of sampling strategies and their parameter are listed here:
...
@@ -47,17 +47,17 @@ All types of sampling strategies and their parameter are listed here:
* Which means the variable value is a value like round(loguniform(low, high)) / q) * q
* Which means the variable value is a value like round(loguniform(low, high)) / q) * q
* Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
* Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
* {"_type":"normal","_value":[label, mu, sigma]}
* {"_type":"normal","_value":[mu, sigma]}
* Which means the variable value is a real value that's normally-distributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.
* Which means the variable value is a real value that's normally-distributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.
* Which means the variable value is a value drawn according to exp(normal(mu, sigma)) so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.
* Which means the variable value is a value drawn according to exp(normal(mu, sigma)) so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.
* Which means the variable value is a value like round(exp(normal(mu, sigma)) / q) * q
* Which means the variable value is a value like round(exp(normal(mu, sigma)) / q) * q
* Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
* Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
For good user experience and reduce user effort, we need to design a good annotation grammar.
## Overview
If users use NNI system, they only need to:
To improve user experience and reduce user effort, we design an annotation grammar. Using NNI annotation, users can adapt their code to NNI just by adding some standalone annotating strings, which does not affect the execution of the original code.
The meaning of this example is that NNI will choose one of several values (0.1, 0.01, 0.001) to assign to the learning_rate variable. Specifically, this first line is an NNI annotation, which is a single string. Following is an assignment statement. What nni does here is to replace the right value of this assignment statement according to the information provided by the annotation line.
2. Annotation intermediate in code as:
'''@nni.report_intermediate_result(test_acc)'''
In this way, users could either run the python code directly or launch NNI to tune hyper-parameter in this code, without changing any codes.
3. Annotation output in code as:
## Types of Annotation:
'''@nni.report_final_result(test_acc)'''
In NNI, there are mainly four types of annotation:
-**sampling_algo**: Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an `nni.` identification and a search space type specified in [SearchSpaceSpec](https://nni.readthedocs.io/en/latest/SearchSpaceSpec.html) such as `choice` or `uniform`.
Which means the variable value is a random integer in the range [0, upper).
-**name**: The name of the variable that the selected value will be assigned to. Note that this argument should be the same as the left value of the following assignment statement.
Which means the variable value is a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.
Which means the variable value is a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.
`@nni.function_choice` is used to choose one from several functions.
**Arguments**
- **functions**: Several functions that are waiting to be selected from. Note that it should be a complete function call with arguments. Such as `max_pool(hidden_layer, pool_size)`.
- **name**: The name of the function that will be replaced in the following assignment statement.
`@nni.report_intermediate_result` is used to report intermediate result, whose usage is the same as `nni.report_intermediate_result` in [Trials.md](https://nni.readthedocs.io/en/latest/Trials.html)
### 4. Annotate final result
`'''@nni.report_final_result(metrics)'''`
`@nni.report_final_result` is used to report the final result of the current trial, whose usage is the same as `nni.report_final_result` in [Trials.md](https://nni.readthedocs.io/en/latest/Trials.html)
